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README.md

@@ -127,3 +127,144 @@ pretty_name: AwA-Pose-Lite
 size_categories:
 - 10K<n<100K
 ---
+
+## **Dataset Summary**
+
+AwA Pose, a quadrupedal keypoint detection dataset that offers richer annotations and greater species diversity than existing datasets. The data supports efficiency, advances in research on generalized keypoint detection in animals.
+
+The dataset was explored, filtered, and stored in a [pyarrow type](https://arrow.apache.org/docs/python/index.html). See : [AwA2_dataset_analysis](https://www.kaggle.com/code/radimkzl/awa2-dataset-analysis)
+
+***Original Kaggle dataset:*** [AwA2_dataset](https://www.kaggle.com/datasets/radimkzl/awa2-dataset)
+
+The original data comes from two sources and is described in:
+
+- [A Novel Dataset for Keypoint Detection of
+Quadruped Animals from Images](https://arxiv.org/pdf/2108.13958)
+- [GitHub:  prinik/AwA-Pose](https://github.com/prinik/AwA-Pose/tree/main)
+- [Animals with Attributes 2](https://cvml.ista.ac.at/AwA2/)
+
+*The dataset contains:*
+- Lite version of keypoints dataset
+- Full version of keypoints dataset
+
+*All version contains:*
+- train dataset: 90%
+- validation dataset: 5%
+- test dataset: 5%
+
+## **Description of data in the dataset**
+
+*Class names:*
+- antelope 
+- bobcat
+- buffalo
+- chihuahua
+- collie
+- cow
+- dalmatian
+- deer
+- elephant
+- fox
+- german+shepherd
+- giant+panda
+- giraffe
+- grizzly+bear
+- hippopotamus
+- horse
+- leopard
+- lion
+- moose
+- otter
+- ox
+- persian+cat
+- pig
+- polar+bear
+- rabbit
+- raccoon
+- rhinoceros
+- sheep
+- siamese+cat
+- squirrel
+- tiger
+- weasel
+- wolf
+- zebra
+
+## **Columns description**
+
+| name column| description of column                |
+|:--------------|:--------------------------------|
+| id | id number of records |
+| right_eye | keypoint values [x,y] |
+| right_earbase | keypoint values [x,y] |
+| right_earend | keypoint values [x,y] |
+| right_antler_base | keypoint values [x,y] |
+| right_antler_end | keypoint values [x,y] |
+| left_antler_base | keypoint values [x,y] |
+| left_antler_end | keypoint values [x,y] |
+| left_earbase |  keypoint values [x,y] |
+| left_earend |  keypoint values [x,y] |
+| left_eye | keypoint values [x,y] |
+| nose | keypoint values [x,y] |
+| upper_jaw | keypoint values [x,y] |
+| lower_jaw | keypoint values [x,y] |
+| mouth_end_right | keypoint values [x,y] |
+| throat_base | keypoint values [x,y] |
+| neck_base | keypoint values [x,y] |
+| neck_end | keypoint values [x,y] |
+| back_base | keypoint values [x,y] |
+| back_middle | keypoint values [x,y] |
+| back_end | keypoint values [x,y] |
+| tail_base | keypoint values [x,y] |
+| body_middle_right | keypoint values [x,y] |
+| bbox | bounding box dimension [x1, y1, x2, y2] |
+| mouth_end_left | keypoint values [x,y] |
+| throat_end | keypoint values [x,y] |
+| tail_end | keypoint values [x,y] |
+| front_left_thai | keypoint values [x,y] |
+| front_left_knee | keypoint values [x,y] |
+| front_left_paw | keypoint values [x,y] |
+| front_right_thai | keypoint values [x,y] |
+| front_right_paw | keypoint values [x,y] |
+| front_right_knee | keypoint values [x,y] |
+| back_left_knee | keypoint values [x,y] |
+| back_left_paw | keypoint values [x,y] |
+| back_left_thai | keypoint values [x,y] |
+| back_right_thai | keypoint values [x,y] |
+| back_right_paw | keypoint values [x,y] |
+| back_right_knee | keypoint values [x,y] |
+| belly_bottom | keypoint values [x,y] |
+| body_middle_left | keypoint values [x,y] |
+| name_file | name of file as string |
+| name_class | name of class as string |
+| image_base64s | image as string Base64 |
+| image_width | value of with of image |
+| image_height | value of height of image |
+| image_license | text of licence of image |
+
+## **Notes on data**
+
+&gt; If a *keypoints* contains `[-1.0, -1.0]`, it means that the point is not visible in the image. These points must be masked when training the model.
+
+&gt; Images are stored as a Base64 string. They can be transformed using the function:
+
+```python
+import base64
+import io
+from PIL import Image
+
+def base64_to_img(base64_str):
+    img_bytes = base64.b64decode(base64_str)
+    img_buffer = io.BytesIO(img_bytes)
+    img = Image.open(img_buffer)
+    
+    return img
+```
+
+Find out more in [AwA2_dataset_analysis](https://www.kaggle.com/code/radimkzl/awa2-dataset-analysis#Create-AwA2-dataset-for-Hugging-Face...)
+
+## **Licensing Information**
+
+Data for keypoints is licensed according to [GitHub: prinik/AwA-Pose](https://github.com/prinik/AwA-Pose/tree/main),  this license is [MIT](https://github.com/prinik/AwA-Pose/tree/main?tab=MIT-1-ov-file#readme).
+
+The license for the images is according to [Animals with Attributes 2](https://cvml.ista.ac.at/AwA2/), see data column `image_license `.